Abstract
The real world contains many kinds of complex network. Using influence nodes in complex networks can promote or inhibit the spread of information. Identifying influential nodes has become a hot topic around the world. Most of the existing algorithms used for influential node identification are based on the structure of the network such as the degree of the nodes. However, the attribute information of nodes also affects the ranking of nodes’ influence. In this paper, we consider both the attribute information between nodes and the structure of networks. Therefore, the similarity ratio, based on attribute information, and the degree ratio, based on structure derived from trust-value, are proposed. The trust–PageRank (TPR) algorithm is proposed to identify influential nodes in complex networks. Finally, several real networks from different fields are selected for experiments. Compared with some existing algorithms, the results suggest that TPR more rationally and effectively identifies the influential nodes in networks.
Highlights
Complex networks simplify the complex systems that are found in the real world
In the PolBooks network, degree centrality (DC) has obtained the highest correlation coefficient. We considered both the topology structure of networks and nodes’ attribute information to define the degree ratio and the similarity ratio, the trust-value was proposed
Combined with PageRank, this paper proposed the trust–PageRank algorithm to identify influential nodes based on trust-value
Summary
Complex networks simplify the complex systems that are found in the real world. The research on complex networks can help people to deeply understand them, such as their internal dynamic evolution and for behavior prediction [1,2,3]. Identifying influential nodes in complex networks is an important direction in the field of complex network research [4], and has practical value for information dissemination in real-world networks [5], spread of infectious diseases [6], product promotion [7,8], etc. This task can effectively reduce economic cost and avoid economic loss to a certain extent [9]. Identifying the influential nodes in biological networks [11] can provide auxiliary means for disease treatment and understanding of biological information
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